Five paired observations of variables and are shown in the scatter diagram. A student draws the line as a proposed line of best fit. The coordinates of the five observations, in order of increasing , are , , , and .

Find the coordinates of the mean point, .
Determine whether the student's line is suitable as a line of best fit by eye. Give a reason for your answer.
0
For a sample of students, denotes the number of hours spent revising and denotes the score on a test. The regression line of on is
for . The Pearson product-moment correlation coefficient is . For this sample size, the critical value for testing linear correlation is .
State the direction and strength of the linear correlation.
Determine whether the linear correlation is significant, giving a reason.
Use the regression line to estimate the test score for a student who revised for hours.
Explain why using this regression line to estimate the score for a student who revised for hours may be unreliable.
0
The height, cm, of a young plant weeks after it is planted is modelled by the regression line
for .
Interpret the meaning of the gradient in this context.
Interpret the meaning of the intercept, and comment on its validity in this context.
Find the predicted height of the plant after weeks.
Find the predicted increase in height over any week interval.
0
Five bivariate observations have variables and . For these observations,
A line of best fit drawn by eye passes through the mean point and also through the point .
Find the coordinates of the mean point.
Find the equation of this line of best fit in the form .
Use your line to estimate when .
0
For a set of bivariate data, the regression line of on is
The observed values of satisfy .
State the direction of the linear correlation suggested by the regression line.
Use the regression line to estimate when .
Find the predicted change in when increases by .
Classify a prediction made using and a prediction made using as interpolation or extrapolation.
0
For a set of bivariate data, the mean point is . The regression line of on has gradient .
Determine the equation of the regression line of on .
Use your equation to predict when .
The regression line of on is . Verify that this line also passes through the mean point.
0
A juice bar recorded the daily maximum temperature, , and the number of cold drinks sold, , on six different days. The data are shown in the table.
Maximum temperature [°C] | Cold drinks sold |
|---|---|
12 | 78 |
14 | 81 |
16 | 92 |
18 | 101 |
20 | 107 |
22 | 117 |
Find Pearson's product-moment correlation coefficient, , for the data.
The regression line of on has equation . Find the value of and the value of .
Use the regression line to estimate the number of cold drinks sold on a day when the maximum temperature is .
0
A teacher recorded the number of absences, , and the final test score, , for six students in a class. The data are shown in the table.
1 | 2 | 3 | 4 | 5 | 6 | |
|---|---|---|---|---|---|---|
number of absences, x | 1 | 2 | 3 | 4 | 5 | 6 |
final test score, y | 75 | 74 | 68 | 62 | 56 | 52 |
Find Pearson's product-moment correlation coefficient, .
Find the equation of the regression line of on .
State what the gradient means in this context.
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An athletics coach records the number of training sessions per week, , and the distance run in a fitness test, km, for six athletes. The data are shown in the table. For , the critical value of at the significance level is .
Variable | Athlete 1 | Athlete 2 | Athlete 3 | Athlete 4 | Athlete 5 | Athlete 6 |
|---|---|---|---|---|---|---|
training sessions per week, x | 1 | 2 | 3 | 4 | 5 | 6 |
distance run, y [km] | 1 | 3 | 4 | 7 | 9 | 10 |
Find Pearson's product-moment correlation coefficient, .
Determine whether there is significant positive linear correlation at the level.
The coach claims that increasing the number of training sessions causes athletes to run further in the test. Comment on this claim.
0
In an investigation, represents temperature and represents the time taken for a reaction. The regression line of on passes through the mean point and has gradient .
Find the equation of the regression line in the form .
Use the regression line to estimate when .
For one observation, and the actual value of was . Find the difference actual value minus predicted value.
Explain why this regression line alone does not prove that temperature causes a change in reaction time.
0
A set of bivariate data has Pearson product-moment correlation coefficient . The observed values of satisfy . A scatter diagram of the data shows points lying close to a curve shaped like a parabola.
State what the value of suggests about the linear correlation.
Explain why the value of may be misleading if used on its own.
State whether a linear regression model would be appropriate for these data, giving a reason.
Explain why a prediction made using would be particularly unreliable.
0
For a set of bivariate data, the regression line of on is
and the regression line of on is
Find the mean point of the data.
Use the appropriate regression line to estimate when .
Explain why rearranging the regression line of on is not the appropriate method for part (b).
0
For a set of data, the regression line of on is
The regression line of on is
The observed values of satisfy .
Use the appropriate regression line to estimate when .
State whether using the regression line of on when would be interpolation or extrapolation.
Use the appropriate regression line to estimate when .
Explain why the equation should not be rearranged to answer part (c).
0
For a set of bivariate data, the regression line of on is
and the regression line of on is
Find the mean point of the data.
Use the appropriate regression line to estimate when .
Use the appropriate regression line to estimate when .
0
For a data set, the regression line of on is
and the regression line of on is
Find the mean point of the data.
Use the appropriate regression line to estimate when .
Interpret the gradient of the regression line of on .
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A scientist models the relationship between a variable and a positive variable . The table gives values of and . The regression equation of on is written as .
Observation | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
x | 2 | 4 | 6 | 8 | 10 |
ln y | 1.182 | 1.647 | 2.112 | 2.577 | 3.042 |
Find the value of and the value of .
Use the regression equation to estimate when .
State whether this estimate is an interpolation or an extrapolation.
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A taxi company records the distance of a journey, km, and the fare, dollars, for five journeys. The data are shown in the table.
Journey | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
distance [km] | 4.491 | 5.921 | 7.600 | 9.779 | 12.209 |
fare [dollars] | 9 | 13 | 17 | 21 | 25 |
Find Pearson's product-moment correlation coefficient, .
Find the regression line of on , in the form .
Use your regression line to estimate the distance of a journey with fare dollars.
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A sports scientist records the age, , in decades, and sprint speed, , in , of five athletes. The data are shown in the table.
Athlete | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
Age / decades | 3 | 4 | 5 | 6 | 7 |
Sprint speed / | 40.91 | 34.39 | 34.20 | 29.79 | 31.71 |
Find the regression line of on .
Find the regression line of on .
Estimate the age, in decades, of an athlete whose sprint speed is . State which regression line you used.
0
For a set of bivariate data, the regression line of on is and Pearson's product-moment correlation coefficient is . New variables and are defined by and .
Determine Pearson's product-moment correlation coefficient between and .
Find the regression line of on .
0
A small museum recorded the number of days, , after the opening of an exhibition and the number of visitors, , on that day. A linear regression model for the data is
The mean of the recorded -values is , and there were recorded days.
Find the mean number of visitors per recorded day.
Hence find the total number of visitors on the recorded days.
Use the regression model to estimate the number of visitors on day .
Find the predicted change in the number of visitors over any -day interval.
On day the actual number of visitors was . Find the residual, defined as actual value minus predicted value.
State whether using the model to estimate the number of visitors on day would be interpolation or extrapolation.
The museum manager claims that each extra day after opening causes exactly fewer visitors. Comment on this claim.
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A shop selling second-hand cameras records the age, years, and the repair cost, dollars, for six cameras. For these data,
A line of best fit drawn by eye must pass through the mean point. A student suggests the line .
Find the coordinates of the mean point.
Determine whether the student's line satisfies the mean point condition.
Use the student's line to estimate the repair cost for a camera aged years.
The regression line of on for these data is , for . Interpret the gradient in context.
Use the regression line to estimate the repair cost for a camera aged years, and comment on the reliability of this estimate.
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A city council records the floor area, , in hundreds of square metres, and the annual number of visits, , in thousands, for several community libraries. For these data, Pearson's product-moment correlation coefficient is . For this sample size, the critical value for testing linear correlation is .
The regression line of on is
(a)(i) Describe the direction and strength of the linear correlation.
(a)(ii) Determine whether the linear correlation is significant, giving a reason.
(b)(i) Estimate the annual number of visits for a library with floor area hundreds of square metres.
(b)(ii) Interpret the intercept of the regression line, and comment on its validity in this context.
(c)(i) A councillor claims that increasing the floor area causes an increase in the number of visits. Comment on this claim.
(c)(ii) Explain why the regression line should not automatically be rearranged to estimate from a given value of .
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For a sample of adults, denotes the number of phone notifications received in one hour and denotes the number of minutes of uninterrupted reading in that hour. The regression line of on has gradient and passes through the mean point . The observed values of satisfy .
Find the equation of the regression line of on , in the form .
Interpret the regression line in the following parts.
Interpret the gradient in context.
Interpret the intercept, and comment on whether this interpretation is reliable.
Use the regression line to answer the following parts.
Estimate the uninterrupted reading time for an adult receiving notifications in one hour.
For one adult with notifications, the actual uninterrupted reading time was minutes. Find the residual, actual value minus predicted value. If you did not obtain an estimate in part (c)(i), use minutes.
Explain why the regression equation alone does not prove that phone notifications cause reduced uninterrupted reading time.
0
A craft workshop records the drying time, hours, and the percentage moisture remaining, , for a set of clay tiles. A regression model for predicting from is
For these data, . For this sample size, the critical value for testing linear correlation is .
State the variable that should be used as the predictor in this regression model.
State the variable being predicted by this regression model.
Use the model to estimate the percentage moisture remaining after hours.
student rearranges the model to estimate the drying time when percent moisture remains. Find the student's estimate, and state one limitation of this method.
Determine whether the linear correlation is significant, giving a reason.
Explain why using the model at hours may be unreliable.
0
For a set of bivariate data, the regression line of on is
The mean point of the data is . The regression line of on has gradient . A student rearranges the regression line of on to obtain and uses this to predict from .
Use the regression line of on to predict when .
Find the equation of the regression line of on .
For , find the prediction from the regression line of on and the prediction from the student's rearranged equation.
State which prediction in part (c) should be used, giving a reason.
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A bicycle hire company records the number of sunshine hours, , and the number of bicycles hired, , on seven Saturdays. The paired data are

Find Pearson's product-moment correlation coefficient, .
Comment on the strength and direction of the linear correlation.
Find the equation of the regression line of on in the form .
Use the regression line to estimate the number of bicycles hired on a Saturday with sunshine hours.
manager wants to use the regression line to estimate the number of bicycles hired on a Saturday with sunshine hours. State whether this would be interpolation or extrapolation, and comment on its reliability.
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An events organiser records the rainfall, mm, and the attendance, , at seven outdoor performances. The paired data are
For , the critical value of at the significance level is .

Find Pearson's product-moment correlation coefficient, .
Determine whether there is significant linear correlation at the level.
Find the regression line of on .
Estimate the attendance when the rainfall is mm.
State whether the estimate in part (b)(ii) is interpolation or extrapolation.
The organiser claims that rainfall causes lower attendance. Comment on this claim.
0
A city engineer records the traffic flow, , in hundreds of vehicles per hour, and the noise level, dB, at six roads. The paired data are

Find the coordinates of the mean point .
Explain why a line of best fit drawn by eye should pass close to this point.
Find the regression line of on .
Use the regression line to estimate the noise level when .
At another road, the traffic flow was and the actual noise level was dB. Find the residual, using actual value minus predicted value, and interpret it.
0
The table shows five paired observations of variables and .
2.4 | 6.971 |
4.4 | 9.693 |
5.4 | 12.600 |
6.4 | 15.507 |
8.4 | 18.229 |
Find the regression line of on .
Find the regression line of on .
The two regression lines intersect at the mean point of the data. Determine the coordinates of this point.
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A marine biologist records the length, cm, and mass, g, of six fish. One observation appears to be unusual. The data are shown in the table.
Observation | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
Length x [cm] | 1 | 2 | 3 | 4 | 5 | 6 |
Mass y [g] | 2.00 | 4.23 | 6.10 | 8.21 | 9.96 | 3.00 |
Find Pearson's product-moment correlation coefficient, , using all six observations.
The biologist removes the unusual observation and recalculates the correlation coefficient using the remaining five observations. Find the new value of .
Using only the remaining five observations, the regression line of on is . Estimate the mass of a fish of length cm.
Comment on the reliability of the estimate in part (c).
0
The population of a bacterial culture, in thousands, is recorded at time hours. The table gives values of and . It is suggested that may be modelled by , where and are constants.
t [h] | ln P |
|---|---|
0 | 1.108 |
2 | 1.472 |
4 | 1.836 |
6 | 2.200 |
8 | 2.564 |
Find the regression equation of on in the form .
Hence find the value of and the value of .
Use the model to estimate the time at which the population reaches thousand.
0
The table shows values of and for seven observations. The table suggests a curved relationship.
-3 | 9.18 |
-2 | 4.14 |
-1 | 1.10 |
0 | 0.08 |
1 | 1.06 |
2 | 4.06 |
3 | 9.08 |
Find Pearson's product-moment correlation coefficient, , for and .
Explain why the value of may be misleading in this context.
Let . Find the regression line of on .
Use the regression line in part (c) to estimate when .
0
A museum records the age years of ceramic samples and their measured hardness on a standard scale. The regression line of on has gradient and passes through the mean point. The mean age is years and the Pearson correlation coefficient is .

Find the mean hardness of the samples if the regression line of on is .
Find the equation of the regression line of on .
Estimate the hardness of a sample aged years. If you did not obtain an equation in part (a)(ii), use .
Estimate the age of a sample with hardness .
Find the ratio of the sample standard deviations.
Explain why the intercept should be interpreted with care.
0
A harbour officer records the time, hours after midnight, and the depth of water, metres, at six times during one tide cycle. The observations are
Find .
Find .
For these data, and . Calculate Pearson's product-moment correlation coefficient, .
State what the value of suggests about the linear correlation.
Explain why using only the value of may be misleading for these data.
State whether a linear regression model is appropriate for these data, giving a reason.
0
For a set of bivariate data, the regression line of on has equation
The regression line of on has equation
The mean of the -values is .
Find the value of .
Write down the mean point.
Use the appropriate regression line to estimate when . If you did not obtain , use .
Use the appropriate regression line to estimate when .
student rearranges and uses it to predict when . Compare this prediction with the prediction from part (b)(i), and state which should be used.
0
A courier company records the distance of a delivery, km, and the delivery time, minutes. The regression line of on is
The regression line of on has gradient . The mean point of the data is . The observed range of is and the observed range of is ; in each case, the endpoints are the observed minimum and maximum values.
Find the equation of the regression line of on .
Verify that the regression line of on passes through the mean point.
Use the appropriate regression line to estimate the delivery time for a distance of km.
Use the appropriate regression line to estimate the distance for a delivery time of minutes. If you did not obtain an equation in part (a)(i), use .
Explain why rearranging is not the appropriate method for part (b)(ii).
Classify the two predictions in part (b) as interpolation or extrapolation.
State why the regression equations alone do not prove that distance causes longer delivery time.
0
A factory records the age, years, of several machines and the number of maintenance hours, , required in one month. The mean point is . The regression line of on has gradient , and the regression line of on has gradient .
Find the equation of the regression line of on .
Find the equation of the regression line of on .
Use the appropriate regression line to estimate the monthly maintenance hours for a machine aged years. If you did not obtain an equation in part (a)(i), use .
Use the appropriate regression line to estimate the age of a machine requiring maintenance hours in one month. If you did not obtain an equation in part (a)(ii), use .
supervisor rearranges the regression line of on to predict from . Compare the supervisor's prediction for with the prediction in part (b)(i), and state which prediction should be used.
0
For a set of bivariate data, the regression line of on is
and the regression line of on is
The mean of the -values is . The observed values satisfy and .
Find the mean of the -values.
Find the value of .
Use the appropriate regression line to estimate when . If you did not obtain , use .
Use the appropriate regression line to estimate when .
Comment on the reliability of the two estimates in part (b), using the observed ranges.
0
A researcher records the altitude, metres, of several mountain villages and the average boiling temperature of water, , in those villages. The regression line of on is
and the regression line of on is
For these data, . For this sample size, the critical value for testing linear correlation is .
Verify that the point lies on the regression line of on .
Verify that the point lies on the regression line of on .
Use the appropriate regression line to estimate the boiling temperature at an altitude of metres.
Use the appropriate regression line to estimate the altitude of a village where the average boiling temperature is .
Determine whether the linear correlation is significant, giving a reason.
State the direction of the correlation.
Interpret the gradient of the regression line of on in context.
Explain why the regression analysis alone does not establish a causal mechanism.
0
A biologist investigates the relationship between two positive variables and . The values of and for six observations are
The regression line of on is written as

Find the value of and the value of .
Assume that the relationship between and can be modelled by , where .
Show that and .
Hence find the value of and the value of .
Use the fitted regression line for to estimate when .
State whether this estimate is interpolation or extrapolation.
If you did not obtain a model in part (b), use . Estimate the value of when .
0
A researcher records values of two variables and . The paired data are

Find Pearson's product-moment correlation coefficient, , for and .
Explain why this value of may be misleading if used on its own.
For each observation, define .
Find the regression line of on .
Hence estimate when .
0
An energy company records the monthly electricity consumption, kWh, and the monthly bill, dollars, for six households. The paired data are

Find the regression line of on .
Find the regression line of on .
Use the appropriate regression line to estimate the electricity consumption for a household whose bill is dollars.
student rearranges the regression line of on to estimate from a value of . Explain why this is not the appropriate method.
0
A drone manufacturer tests six batteries. The ambient temperature, , and the flight time, minutes, are recorded as

Find the regression line of on .
Find the regression line of on .
Find the coordinates of the point of intersection of the two regression lines.
If you did not obtain regression lines in part (a), use and .
Estimate the flight time when the ambient temperature is .
Estimate the ambient temperature when the flight time is minutes.
0
A conservation laboratory studies the fading of a pigment. The time, years, and the natural logarithm of the brightness index, , are recorded for six samples:
It is suggested that can be modelled by , where and .

Find the regression equation of on in the form .
Hence find the value of and the value of .
If you did not obtain a model in part (b), use .
Estimate the time at which the brightness index is .
State whether the estimate in part (c) is interpolation or extrapolation.
Give one limitation of this model.
0
A jeweller records the diameter, mm, and mass, mg, of six gemstones:

Find the regression line of on .
Find the regression line of on .
If you did not obtain regression equations in part (a), use and for subsequent calculations.
Estimate the mass of a gemstone with diameter mm.
Estimate the diameter of a gemstone with mass mg.
student uses the equation to estimate the diameter of a gemstone with mass mg. Compare this method with the method used in part (b)(ii).
0
A manufacturer tests rechargeable cells at different ambient temperatures. Let be the ambient temperature in and let be the discharge time in hours. For a sample of cells, the following summary statistics are obtained:
A scatter diagram suggests that a linear model is reasonable for temperatures between and .

Find the equation of the regression line of on in the form .
Use your regression line to estimate the discharge time at .
Find the equation of the regression line of on in the form .
Estimate the ambient temperature corresponding to a discharge time of hours. If you did not obtain an equation in part (b)(i), use .
Show that the product of the gradients of the two regression lines is equal to .
Explain why rearranging the regression line of on is not the correct method for estimating from a given value of .
0
An environmental officer records the level of fine particles, , and the number of respiratory complaints, , in districts. One district is known to contain a large industrial fire during the recording period. For all districts,
where , and .
After removing the industrial-fire district, the corresponding statistics for the remaining districts are
Statistic | All 8 districts | Remaining 7 districts |
|---|---|---|
8 | 7 | |
Mean | 42 | 38 |
Mean | 18 | 14 |
1246 | 350 | |
1018.5 | 122.5 | |
1099 | 203 |
Calculate Pearson's product-moment correlation coefficient for all districts.
Find the regression line of on using all districts.
Calculate Pearson's product-moment correlation coefficient after removing the industrial-fire district.
Using the remaining districts, find the regression line of on .
Using the model from part (b)(ii), estimate the number of complaints when the particle level is . If you did not obtain an equation in part (b)(ii), use .
The critical values of for a two-tailed test at the level are for and for . Compare the conclusions about significant linear correlation before and after removing the industrial-fire district.
0
A materials laboratory measures the diameter mm and breaking load N of metal rods. Technology gives the regression line of on as
The regression line of on is
The observed loads range from N to N.
Use the stated regression equations, rather than numerical values read from the diagram, for all calculations.

Find the coordinates of the mean point of the data.
Find Pearson's product-moment correlation coefficient, .
Estimate the diameter of a rod with breaking load N. State which regression line you used.
student instead rearranges to estimate the diameter. Find the student's estimate.
Explain why the estimate in part (b)(i) is more appropriate than the student's estimate.
Deduce why the two regression lines would be rearrangements of each other only when .
0
A chemical engineer records the temperature in and the time in minutes for a reaction to reach a fixed yield. Technology gives
as the regression line of on , and
as the regression line of on .
The engineer later uses for temperature in kelvin and for time in seconds.

Find Pearson's product-moment correlation coefficient between and .
Explain why the negative value of is appropriate in this context.
Find the regression line of on .
Use your line to estimate the reaction time in seconds at K. If you did not obtain an equation in part (b)(i), use , retaining unrounded values before final rounding.
Find the regression line of on .
State the correlation coefficient between and , giving a reason.
0
A biologist studies the relationship between salinity and the number of larvae found in water samples. Technology gives the two regression lines
and
There are samples and the observed salinities range from to .

Find Pearson's product-moment correlation coefficient, .
Describe the linear correlation.
Estimate the number of larvae when the salinity is . State whether this is interpolation or extrapolation.
Estimate the salinity when the number of larvae is . State which regression line is used.
For , the critical value of at the significance level is . Determine whether there is significant linear correlation.
The biologist claims that increasing salinity causes the number of larvae to decrease. Comment on this claim.
Explain why using the first regression line to estimate when may be unreliable.
0
A workplace study records background noise level dB and productivity score for offices. Technology gives
For , the critical values of for a two-tailed test are at the level and at the level. A new variable measures noise above dB, and measures productivity loss.

State suitable hypotheses for testing whether there is linear correlation between noise level and productivity score.
Determine whether the correlation is significant at the level and at the level.
Interpret the gradient of the regression line in context.
Explain why the intercept should be interpreted with caution.
Find the regression line of on .
State the correlation coefficient between and , giving a reason.
0
For a set of bivariate data, the regression line of on is
and the regression line of on is
Find the mean point of the data.
Use the appropriate regression line to estimate when .
Use the appropriate regression line to estimate when .
Show algebraically that the two regression lines would be the same line only if their gradients were reciprocals.
Use the gradients in this question to explain why the two regression lines are not the same line.
0
A small wind turbine is tested at different wind speeds. Let be the wind speed and be the power output. The following paired values of and are recorded:

Find the regression line of on in the form .
Hence write a model for in the form .
Find the regression line of on .
Use the appropriate regression equation to estimate the wind speed when the power output is . If you did not obtain an equation in part (b)(i), use .
Explain why rearranging the equation in part (a)(i) is not generally the appropriate method for estimating from a given value of .
0
A property analyst records the floor number, , and the weekly rent, dollars, for seven apartments in the same building:
The apartment on floor has a large private terrace and may be an unusual observation.

Using all seven observations, find Pearson's product-moment correlation coefficient, .
Remove the observation for floor . Find the new value of for the remaining six observations.
Find the regression line of on using all seven observations.
Find the regression line of on using only the first six observations.
Use both regression lines to estimate the weekly rent on floor .
Evaluate the reliability of using these models to predict the rent on floor .
0
A data set has six observations for variables and :
New variables and are defined by
10 | 44 | -2 | 103 |
15 | 52 | -1 | 119 |
20 | 61 | 0 | 137 |
25 | 69 | 1 | 153 |
30 | 80 | 2 | 175 |
35 | 87 | 3 | 189 |
Find Pearson's product-moment correlation coefficient for and .
Find the regression line of on .
Determine Pearson's product-moment correlation coefficient for and .
Find the regression line of on .
If you did not obtain an equation in part (b)(ii), use . Estimate when .
0
A light sensor is moved to positions at signed distances cm from the centre of a lamp. The recorded intensity is . The scatter diagram is symmetric about the -axis. For the data collected, the transformation gives an exact linear relationship
The observed values of are equally spaced on both sides of , and no observation is made at .
x [cm] | y [a.u.] | z [cm^2] |
|---|---|---|
-3 | 10 | 9 |
-2 | 5 | 4 |
-1 | 2 | 1 |
1 | 2 | 1 |
2 | 5 | 4 |
3 | 10 | 9 |
Explain why the Pearson correlation coefficient between and is .
Explain why does not mean that there is no relationship between and .
Use the regression of on to estimate the intensity when .
State whether this estimate is an interpolation or an extrapolation, given that lies within the observed range of .
Suppose a data set consists of pairs and , where . Show that the Pearson correlation coefficient between and is , provided the variance of and the variance of are non-zero.
0
A meteorologist studies the relationship between wind speed km h and a cooling index . For the sample,
The plotted observations are illustrative and rounded; use the supplied summary statistics for all calculations.
A family of straight lines through the mean point is considered:

Find Pearson's product-moment correlation coefficient, .
Find the regression line of on .
Show that the sum of squared vertical residuals for the line is
Hence find the value of that minimizes .
Find the minimum possible value of . If you did not obtain , use this value.
For an observation with and , find the residual using the regression line of on .
0
A geologist records the mineral density and magnetic response of rock samples. For one analysis, the regression line of on is
and the mean point is . The Pearson correlation coefficient is .

Verify that the given regression line passes through the mean point.
Find the equation of the regression line of on .
second report claims that the regression line of on is while the regression line of on remains . Explain why this cannot be correct.
Use the correct regression line of on to estimate when . If you did not obtain an equation in part (a)(ii), use .
Deduce why, unless , the regression line of on should not be rearranged to predict from a given .
0
A researcher studies the relationship between the number of practice problems attempted, , and a performance score, . For the first students,
A seventh student, who attempted many practice problems but had a relatively low score, is added to the data set. This student's values are .

For the first students, find Pearson's correlation coefficient.
Find the regression line of on for the first students.
Find the new mean point after the seventh student is added.
Show that the updated values are
For all students, find the correlation coefficient and the regression line of on . If you did not show the updated values in part (b)(ii), use them as given.
Compare the predicted score for using the original model and the updated model.
0
A nutrition study records daily fibre intake grams and a health score . The participants belong to two age groups, A and B. For group A,
For group B,

Find the correlation coefficient within each group.
Find the regression line of on within group A.
Find the combined mean point for all participants.
Show that, for the combined data, , and .
Find the correlation coefficient and the regression line of on for the combined data.
Discuss why the combined correlation may lead to a misleading interpretation of the relationship between fibre intake and health score.
0